For our first attempt, let us build a very simple Multi-layer Perceptron (MLP) for our problem. Below is the code to build an MLP model inkeras: # define variables input_num_units = 784 hidden_num_units = 50 ou
可以发现,CNN训练耗费的时间比较长,但其性能优异。 胶囊网络(Capsule Network) 胶囊网络的结构比CNN网络更加复杂,下面构建胶囊网络模型,代码如下: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 defCapsNet(input_shape,n_class,routings):x=layers.Input(shape=input_shape)# Layer1:Just a conventional Conv...
从表中我们可以看出,当我们用单标签数据对模型进行训练,并在多标签数据上进行测试时,capsule network的性能远远高于LSTM、CNN等。 此外,我们还做了case study分析,发现路由参数可以表示胶囊的重要性,并对胶囊进行可视化(此处我们主要可视化3-gram的结果)。具体来说,我们删除卷积胶囊层,将primary capsule layer直接链接到...
我们知道,卷积层是 CNN 的重要组成部分,它会尽可能的去检测出重要的模式,然后逐渐的形成 high level 的 feature,然后将这些 feature 继续编辑成更加 high level 的 feature。然后用 fc layer 输出最终的分类结果。 一个重要的信息是:高层的 feature 是在底层特征基础上 加权求和得到的:activations of a precedings...
Moreover, the capsule network and its variants only explore the PrimaryCaps layer for various reasoning tasks while ignoring the specific information learned by the low-level convolutional layers. In this paper, we propose a Graph Routing based on Multi-head Pairwise-relation Attention (GraMPA) ...
capsules with a dimension\(d^{1}\)of 8. Multiple fully-connected capsule layers can be added to increase the capacity of the network. However, we adopt only two layers of capsules due to the relative simplicity of the dataset investigated. Finally, the output layer of the network has a ...
capsule_layer代码实现如下: 但是这样子在没有pooling或者设置stride大于1的情况下仍然参数量巨大,因为pooling和stride大于1的设置对网络的缩减是成倍缩小的,虽然这可能违背capsule作者Hinton的模型设计初衷,但是本文还是尝试实现,探究其效果如何。 实验结果以及分析 ...
is used as input of the multilane Capsule Network (CapsNet). In this study, the multilane CapsNet contains eight lanes with differently sized 1D convolution kernels, and each CapsNet lane contains a convolutional layer (Conv), a convolutional capsule layer (PrimaryCaps) and a HiddenCaps layer. ...
A feature extractor, which is part of a pretrained image classification CNN, prevents the network from overfitting and improves its performance on both seen and unseen attacks. A statistical pooling layer, which is used in each primary capsule of the network, greatly reduces the number of paramete...
CapsNet [15] is a three-layer simple network, with one convolution layer and two capsule layers. This network is not suitable for VOC, COCO and other complex datasets, because the parameters are too large. We need to design a new capsule network model with acceptable parameters, and the new...